Scene representations convert visual sensory data into compact forms. Recent trends [1]}, [2]} show that the mapping function \(\mathbf {y} = f(\mathbf {x};\theta )\)  between the spatial coordinate \(\mathbf {x}\)  and the scene property \(\mathbf {y}\)  can serve as an implicit scene representation parameterized by a single neural network \(\theta \) . Such a new paradigm has drawn significant attention: the neural network defined in a continuous and differentiable function space can be trained to recover fine-grained details at scene scale with efficient memory consumption, which offers great benefits over alternatives.
